16 citations · 17 across the 4 of their papers we have counts for
4 papers
Verification-Aware Training for Speculative Decoding
Geonmo Gu, Byeongho Heo, HeeJae Jun +4
Speculative decoding accelerates large language model inference by using a draft model to generate candidate tokens, which are verified by the target model in a single forward pass…
MuCo: Multi-turn Contrastive Learning for Multimodal Embedding Model
Geonmo Gu, Byeongho Heo, Jaemyung Yu +7
Universal Multimodal embedding models built on Multimodal Large Language Models (MLLMs) have traditionally employed contrastive learning, which aligns representations of query-targ…
Language-only Efficient Training of Zero-shot Composed Image Retrieval
Geonmo Gu, Sanghyuk Chun, Wonjae Kim +2
Composed image retrieval (CIR) task takes a composed query of image and text, aiming to search relative images for both conditions. Conventional CIR approaches need a training data…
CompoDiff: Versatile Composed Image Retrieval With Latent Diffusion
Geonmo Gu, Sanghyuk Chun, Wonjae Kim +3
This paper proposes a novel diffusion-based model, CompoDiff, for solving zero-shot Composed Image Retrieval (ZS-CIR) with latent diffusion. This paper also introduces a new synthe…